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1. Towards an Analysis of the Architecture, Security, and Privacy Issues in Vehicular Fog Computing

   Hoque, Mohammad Aminul ; Hasan, Ragib ( April 2019 , Proceedings of IEEE Southeastcon)

   The vehicular fog is a relatively new computing paradigm where fog computing works with the vehicular network. It provides computation, storage, and location-aware services with low latency to the vehicles in close proximity. A vehicular fog network can be formed on-the-fly by adding underutilized or unused resources of nearby parked or moving vehicles. Interested vehicles can outsource their resources or data by being added to the vehicular fog network while maintaining proper security and privacy. Client vehicles can use these resources or services for performing computation-intensive tasks, storing data, or getting crowdsource reports through the proper secure and privacy-preserving communication channel. As most vehicular network applications are latency and location sensitive, fog is more suitable than the cloud because of the capability of performing calculations with low latency, location awareness, and the support of mobility. Architecture, security, and privacy models of vehicular fog are not well defined and widely accepted yet as it is in its early stage. In this paper, we have analyzed existing studies on vehicular fog to determine the requirements and issues related to the architecture, security, and privacy of vehicular fog computing. We have also identified and highlighted the open research problems in this promising area.
2. Privacy-aware estimation of relatedness in admixed populations

   https://doi.org/10.1093/bib/bbac473  

   Wang, Su ; Kim, Miran ; Li, Wentao ; Jiang, Xiaoqian ; Chen, Han ; Harmanci, Arif ( November 2022 , Briefings in Bioinformatics)

   Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.

   Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.

   Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness ofmore »individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.

   Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.

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3. A cloud-based secure and privacy-preserving clustering analysis of infectious disease

   J. Liu, Y. Hu ( September 2018 , The Second IEEE Symposium on Privacy-Aware Computing (PAC'18))

   The early detection of where and when fatal infectious diseases outbreak is of critical importance to the public health. To effectively detect, analyze and then intervene the spread of diseases, people's health status along with their location information should be timely collected. However, the conventional practices are via surveys or field health workers, which are highly costly and pose serious privacy threats to participants. In this paper, we for the first time propose to exploit the ubiquitous cloud services to collect users' multi-dimensional data in a secure and privacy-preserving manner and to enable the analysis of infectious disease. Specifically, we target at the spatial clustering analysis using Kulldorf scan statistic and propose a key-oblivious inner product encryption (KOIPE) mechanism to ensure that the untrusted entity only obtains the statistic instead of individual's data. Furthermore, we design an anonymous and sybil-resilient approach to protect the data collection process from double registration attacks and meanwhile preserve participant's privacy against untrusted cloud servers. A rigorous and comprehensive security analysis is given to validate our design, and we also conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of communication and computing overhead.
4. Dealer: an end-to-end model marketplace with differential privacy

   https://doi.org/10.14778/3447689.3447700  

   Liu, Jinfei ; Lou, Jian ; Liu, Junxu ; Xiong, Li ; Pei, Jian ; Sun, Jimeng ( February 2021 , Proceedings of the VLDB Endowment)

   Data-driven machine learning has become ubiquitous. A marketplace for machine learning models connects data owners and model buyers, and can dramatically facilitate data-driven machine learning applications. In this paper, we take a formal data marketplace perspective and propose the first en D -to-end mod e l m a rketp l ace with diff e rential p r ivacy ( Dealer ) towards answering the following questions: How to formulate data owners' compensation functions and model buyers' price functions? How can the broker determine prices for a set of models to maximize the revenue with arbitrage-free guarantee, and train a set of models with maximum Shapley coverage given a manufacturing budget to remain competitive ? For the former, we propose compensation function for each data owner based on Shapley value and privacy sensitivity, and price function for each model buyer based on Shapley coverage sensitivity and noise sensitivity. Both privacy sensitivity and noise sensitivity are measured by the level of differential privacy. For the latter, we formulate two optimization problems for model pricing and model training, and propose efficient dynamic programming algorithms. Experiment results on the real chess dataset and synthetic datasets justify the design of Dealer and verify the efficiencymore »and effectiveness of the proposed algorithms.« less
5. Weaving Privacy and Power: On the Privacy Practices of Labor Organizers in the U.S. Technology Industry

   SAYASH KAPOOR, MATTHEW SUN ( May 2022 , Computer supported cooperative work CSCW)

   We investigate the privacy practices of labor organizers in the computing technology industry and explore the changes in these practices as a response to remote work. Our study is situated at the intersection of two pivotal shifts in workplace dynamics: (a) the increase in online workplace communications due to remote work, and (b) the resurgence of the labor movement and an increase in collective action in workplaces— especially in the tech industry, where this phenomenon has been dubbed the tech worker movement. The shift of work-related communications to online digital platforms in response to an increase in remote work is creating new opportunities for and risks to the privacy of workers. These risks are especially significant for organizers of collective action, with several well-publicized instances of retaliation against labor organizers by companies. Through a series of qualitative interviews with 29 tech workers involved in collective action, we investigate how labor organizers assess and mitigate risks to privacy while engaging in these actions. Among the most common risks that organizers experienced are retaliation from their employer, lateral worker conflict, emotional burnout, and the possibility of information about the collective effort leaking to management. Depending on the nature and source of themore »risk, organizers use a blend of digital security practices and community-based mechanisms. We find that digital security practices are more relevant when the threat comes from management, while community management and moderation are central to protecting organizers from lateral worker conflict. Since labor organizing is a collective rather than individual project, individual privacy and collective privacy are intertwined, sometimes in conflict and often mutually constitutive. Notions of privacy that solely center individuals are often incompatible with the needs of organizers, who noted that safety in numbers could only be achieved when workers presented a united front to management. Based on our interviews, we identify key topics for future research, such as the growing prevalence of surveillance software and the needs of international and gig worker organizers. We conclude with design recommendations that can help create safer, more secure and more private tools to better address the risks that organizers face.« less